Video applications require that large amounts of data be transmitted at high bit rates and with a minimal amount of signal distortion. Since the available data bandwidth of a conventional transmission channel is limited, image coding techniques are utilized to compress the large amount of digital data to fit within the limited bandwidth.
Various video compression techniques are known in the art, such as those of the joint photographic expert group (JPEG), moving picture expert group (MPEG), such the MPEG-1, MPEG-2, and MPEG-4, H-compression, such as the H.261 H.262, H.263, and H.264, and the others. In most of these compression techniques, an image to be compressed is first divided into square blocks of pixels (e.g., an 8×8 pixel block). Each of these blocks is then transformed using discrete cosine transforms (DCT) into a transformed block (with 8×8-components) containing the DCT coefficients. These transformed blocks are then quantized (i.e. limited to one of a fixed set of possible values), and run-length encoded. Often, they are also variable length coded to further reduce the statistical redundancy present in the run-length coded data. A decoder on the receiving end of the transmission reconstructs the video stream from the transmitted, compressed signals.
As broadcast systems are required to provide an ever increasing amount of data utilizing the same data bandwidth, video signals are transmitted at lower and lower bit rates. For example, to increase the number of TV channels broadcasted to the viewers over a fixed data bandwidth, the bit rate of each channel is reduced to a rate between 1.6 Mbps to 2.2 Mbps. Unfortunately, transmitting data at too low a bit rate reduces the quality of the decompressed video stream. Furthermore, distortions are introduced into the decoded image, mainly consisting of annoying visual artifacts that are especially noticeable at medium and low bit rates. Distortions can be categorized into types, including “blocking” (or “blockness”), “blurring”, and “wiggles”, examples of which are shown in FIGS. 1A, 1B and 1C, to which reference is now made.
The blocking effect introduces artificial edges at the boundaries of the 8×8-pixel block, due to the quantization of the transform coefficients in each block. FIG. 1A is an image of a man's face. Unfortunately, the coloring of his face in the area marked 10 is “blocky” rather than smooth. The edges of the blocks are perceived by the human eye as unnatural geometrical contours.
Quantization of transform coefficients also causes blurring of real contours present in the image, due to the reduction of the high frequency components in the DCT transformed blocks. In FIG. 1B, the areas labeled 12 are blurred.
Distortion has another side effect, where some retained frequency components remain unbalanced, causing ripples near edges. These ripples, known as “wiggles” or “mosquito noise”, cause those areas with high frequency components to appear, move and disappear at random points of the frame. This can be seen in FIG. 1C, in the areas labeled 14.
Decompressed video signals may include other noise effects as well, such as blotches and ringing.
It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements.